VP, AI Enablement & Agent Platform

Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world's health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient's request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health. By joining Datavant today, you're stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare. Role Overview The VP of AI Enablement & Agent Platform owns the infrastructure, standards, and organizational enablement required to deploy AI agents at enterprise scale. This includes agent hosting and orchestration infrastructure, MCP server architecture, reusable AI components and reference architectures, and the engineering standards that govern how agents are built, deployed, and operated in production. This is distinct from Datavant's ML Platform organization, which owns model training, feature stores, and data science infrastructure. This role owns the application layer, the infrastructure and standards that turn models into production agent systems. This is not a strategy-only position. The VP is expected to build and ship platform infrastructure, make definitive architectural decisions, and lead a team capable of operating critical AI infrastructure in a regulated healthcare environment. Key Responsibilities AI Agent Infrastructure & Orchestration • Own the design, build, and operation of Datavant's AI agent hosting and orchestration platform, including inference infrastructure, agent runtimes, compute provisioning, and API gateway services • Architect multi-agent orchestration capabilities that support the full autonomy spectrum, from human-in-the-loop copilots to fully autonomous workflow agents, with appropriate controls at each level • Own agent lifecycle management: versioning, deployment, rollback, monitoring, and decommissioning of production agents • Establish infrastructure patterns that decouple agent logic from underlying model providers, enabling model portability and vendor flexibility • Drive cost optimization for inference at scale as agent usage grows across the organization MCP Server Architecture & AI Integration • Own the MCP server architecture that connects AI agents to Datavant's enterprise systems, data stores, and external services • Design and enforce standardized integration patterns, tool catalogs, connector frameworks, and authentication models, that allow agents to interact with enterprise systems securely and consistently • Build reusable MCP servers and integration components that reduce time-to-production for new AI use cases from weeks to days • Establish integration testing, security review, and certification processes for AI system connectors, ensuring agents cannot access systems or data beyond their authorized scope • Partner with infrastructure and security teams to define the boundary between agent-accessible and agent-restricted systems, particularly for environments handling PHI and other regulated data AI Developer Experience & Enablement • Own the internal developer experience for AI — SDKs, CLIs, documentation, templates, reference architectures, and golden paths that make it straightforward for any engineering team to build on the AI platform • Build and maintain a catalog of reusable AI components including agent templates, prompt libraries, evaluation frameworks, and production-ready patterns, that accelerate adoption and reduce duplicated effort • Establish engineering standards for agent development, testing, and production operation, including security requirements, performance benchmarks, and quality gates • Lead enablement programs from workshops and office hours to embedded engineering support for high-priority AI initiatives • Define and track adoption metrics that measure platform utilization, developer productivity impact, and time-to-production • Serve as the central coordination point for AI engineering practices by consolidating duplicated efforts and promoting proven approaches through the platform rather than mandates AI Governance & Production Operations • Establish a multi-layered AI governance framework spanning build-time (secure agent construction and review), deployment (approval gates, environment controls, access scoping), and runtime (behavioral monitoring, anomaly detection, kill switches) • Partner with Security, Legal, Compliance, and Privacy to embed responsible AI practices by design, ensuring agents operate within HIPAA, SOC 2, and FedRAMP readiness requirements • Define and enforce agent permissioning models with scoped data and system access and audit trails for every action • Establish production SRE practices for AI systems including inc